Diversity Matters: Optimal Collision Energies for Tandem Mass Spectrometric Analysis of a Large Set of N-Glycopeptides.

Identification and characterization of N-glycopeptides from complex samples are usually based on tandem mass spectrometric measurements. Experimental settings, especially the collision energy selection method, fundamentally influence the obtained fragmentation pattern and hence the confidence of the database search results ("score"). Using standards of naturally occurring glycoproteins, we mapped the Byonic and pGlyco search engine scores of almost 200 individual N-glycopeptides as a function of collision energy settings on a quadrupole time of flight instrument. The resulting unprecedented amount of peptide-level information on such a large and diverse set of N-glycopeptides revealed that the peptide sequence heavily influences the energy for the highest score on top of an expected general linear trend with m/z. Search engine dependence may also be noteworthy. Based on the trends, we designed an experimental method and tested it on HeLa, blood plasma, and monoclonal antibody samples. As compared to the literature, these notably lower collision energies in our workflow led to 10-50% more identified N-glycopeptides, with higher scores. We recommend a simple approach based on a small set of reference N-glycopeptides easily accessible from glycoprotein standards to ease the precise determination of optimal methods on other instruments. Data sets can be accessed via the MassIVE repository (MSV000089657 and MSV000090218).

Tailoring to Search Engines: Bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence.

Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide-spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptide-level results, we designed methods with one or two liquid chromatography-tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10-40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).

Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine.

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. We found that search engines show a range of behavior between peptide-centric and glycan-centric, which manifests itself already in the dependence of optimal collision energy on m/z. Using classical statistical and machine learning methods, we revealed that peptide hydrophobicity, glycan and peptide masses, and the number of mobile protons also have significant and search-engine-dependent influence, as opposed to a series of other parameters we probed. We envisioned an MS/MS workflow making a smart collision energy choice based on online available features such as the hydrophobicity (described by retention time) and glycan mass (potentially available from a scout MS/MS). Our assessment suggests that this workflow can lead to a significant gain (up to 100%) in the identification confidence, particularly for low-scoring hits close to the filtering limit, which has the potential to enhance reproducibility of N-glycopeptide analyses. Data are available via MassIVE (MSV000093110).

The Effect of Environmental Noise on Consonant Perception in Individual With Normal Hearing: A Prospective Observational Study.

This study was done to identify the effect that environmental noises have on consonant perception of individuals with normal hearing sensitivity. The objectives were to find out the effect of white noise and environmental noises on consonant identification and to find the effect of noises on consonant features. Adult with normal hearing in both ears whose mother tongue was Odia were included. Initially the participants underwent pure tone audiometry, speech audiometry, immittance audiometry to confirm their normal hearing, good speech identification scores and normal middle ear function. For consonant identification test, the presentation level was 40 dB sensation level with reference to speech recognition threshold for all subjects. Consonant identification test was carried out in white noise and real environmental noises (traffic noise, classroom noise, park noise, restaurant noise) at 0 dB signal to noise ratio (SNR) and at + 5 dB (SNR). The results showed that at + 5 dB SNR condition all subjects were able to get 80% and above consonant identification (CI) scores irrespective of type of noise used for stimulus while at 0 dB SNR, the mean scores ranged from 75.5% (restaurant noise) to 84% (traffic noise). At + 5 dB SNR only in restaurant noise the CI scores were low as compared to those in white noise. At 0 dB SNR, the scores were low for both the test conditions of park noise and that of restaurant noise. Different types of noisy environments can affect consonant perception which can affect speech intelligibility.

Development and evaluation of Tamil Matrix Sentence Test Performance in young adults

ABSTRACT Purpose The purpose of the study was to develop the Tamil Matrix Sentence Test (TMST) and evaluate the performance of a group of young adults with normal hearing on the developed test. The developed sentences were also administered at varying intensities to obtain a performance-intensity (PI) function. Methods A base matrix with 10 sentences containing 5 words each with a total of 50 words was used to develop the TMST. The sentences had a fixed semantic sentence structure of Tamil language in the order of noun, number, adjective, object and verb. The developed test consisted of 30 lists with 10 sentences in each list. The performance of 60 young adults with normal hearing aged 18 to 24 years across the 30 lists were compared for list equivalency. To obtain the PI function the sentences were administered on 20 young adults with normal hearing at intensities from 20 dB HL to 100 dB HL in 10 dB increments. The performance across the intensity levels were compared. Results The 30 lists of TMST were found to be acoustically equivalent. However, few lists showed significant difference in the scores obtained on them compared to the rest of the lists. The PI function revealed a saturation in performance beyond 40 dB HL. Conclusion From the results it was construed that TMST can be used to evaluate the speech identification abilities of Tamil speaking listeners. Multiple lists offer the advantage of retesting without the influence of practice or listeners memorizing the test material.

Behavioural and Electrophysiological Correlates of Aided Performance in Individuals with Late Onset Auditory Neuropathy Spectrum Disorder: A Review.

Hearing aids are one of the rehabilitative options for individuals with late onset auditory neuropathy spectrum disorder (ANSD). There is a growing body of evidence that supports the effectiveness of hearing aids among these individuals. The objective of the study was to review the behavioural and electrophysiological factors that determine the effectiveness of amplification in individuals with late onset ANSD. An intensive search was carried out using the PubMed, SpringerLink and Google Scholar databases using the following keywords: auditory neuropathy, late onset ANSD, hearing aids and amplification. There were both retrospective and experimental research studies that showed aided improvement in individuals with late onset ANSD. The aided improvement in these studies has been attributed to preserved neural synchrony. Hence, it was concluded that, hearing aids are effective in improving speech perception in a sub-group of individuals with late onset ANSD. Measures that evaluate neural synchrony are promising tools to identify the sub-group of such individuals. Speech identification scores at higher sensation levels and cortical potentials are the two clinical measures that indicate aided performance in individuals with late onset ANSD.